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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 851–860 of 1746 papers

Title	Date	Tasks	Status	Hype
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense	Sep 11, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices	Sep 8, 2022	Adversarial RobustnessFederated Learning	—Unverified	0
Bispectral Neural Networks	Sep 7, 2022	Adversarial RobustnessRepresentation Learning	CodeCode Available	1
Adversarial Robustness for Tabular Data through Cost and Utility Awareness	Aug 27, 2022	Abuse DetectionAdversarial Robustness	—Unverified	0
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods	Aug 26, 2022	Adversarial RobustnessCode Generation	—Unverified	0
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training	Aug 26, 2022	Adversarial Robustness	—Unverified	0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks	Aug 26, 2022	Adversarial RobustnessParameter Prediction	—Unverified	0
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs	Aug 26, 2022	Adversarial RobustnessFew-Shot Learning	CodeCode Available	0
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression	Aug 26, 2022	Adversarial RobustnessDensity Estimation	—Unverified	0
Shortcut Learning of Large Language Models in Natural Language Understanding	Aug 25, 2022	Adversarial RobustnessNatural Language Understanding	—Unverified	0

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified